In clinical settings, a routinely used method to measure RNA is real-time PCR [51]. However, this grind-and-bind technique is unable to visualize the individual cell signals in their original context, and is prone to becoming contaminated by unintended cell and tissue types and masking the different cellular subpopulations and phenotypes in the heterogenous TME [6,52]. Next-generation sequencing and single-cell sequencing technologies can detect RNA expression at the single cell level, but dissociation from their native setting deprives the data related to their spatial relationship [53]. With the latest developments in RNA ISH, multiple approaches came into play such as non-isotopic fluorescently labeled ISH (fluorescence in situ hybridization—FISH) and biotin or hapten labeled nucleic acid probes (chromogenic in situ hybridization—CISH) to gather spatial data [52,54,55,56,57]. These methods opened a new data dimension, supporting localization and quantitation of target RNA in single cells to detect precise RNA expression in specific cell types [52,58]. However, these techniques only allow a restricted number of labels to be integrated into the probes, leading to reduced sensitivity of expression for most of the genes [52]. Due to a high possibility of cross-hybridization and non-specific binding in complicated tumors, the signal-to-noise ratio is constrained, and extreme technical complication limits the performance of these methods [52,58]. In Figure 1, a representative example of the automated quantitative analysis of FISH and RNA ISH is shown.

RNAscope by Advanced Cell Diagnostics Inc., Hayward, CA (ACD) has presented the most pragmatic method that overcomes these limitations of traditional RNA ISH by a unique probe design and an advanced signal amplification system [52,59]. This technology excels due to its specificity, sensitivity, low turnaround time, and robustness in a wide range of applications across various disciplines including infectious diseases, neuroscience, cell or gene therapy, and single-cell transcriptomic profiling in cancer [52,60,61,62,63,64]. In the TME, RNAscope has prominent advantages such as spatially mapping a cell atlas [65,66], visualizing and characterizing gene signatures and generating the immune landscape, and even identification of novel cell subtypes [67,68], classifying and identifying highly heterogeneous and immunotherapeutic cell types [69,70], and identification and characterization of a gene signature of stem cells [71,72,73] and circulating tumor cells [74,75] as well as analyzing or predicting their response to drug treatments [76,77]. Compared with a one-probe RNA ISH hybridization system, the possibility of nonspecific amplification in RNAscope is considerably low since it implies a double-probe independent hybridization system and improves the sensitivity and the signal-to-noise ratio, allowing better quantification of RNA expression [52,78].

The RNAscope method allows robust detection of mRNA, long non-coding as well as microRNAs [57,79,80,81,82], and multiple gene transcripts generated by alternative splicing [83,84] simultaneously in fresh-fixed, fresh-frozen, and formalin-fixed paraffin-embedded (FFPE) clinical specimens, revealing the full potential of RNA [85]. For example, the expression of a majority of androgen receptor (AR) splice variants other than the full-length AR variant remains unclear in prostate cancer progression. RNAscope has been proposed to be a capable technique for detecting expression and localization of splice variants by designing probes specifically to target distinct splice variants. For example, AR and AR-V7 expression have been detected in FFPE prostate tumors by RNAscope where AR expression was found to be 3-fold higher in primary tumor cells compared with benign glands, while AR-V7 expression was higher in metastatic castration-resistant prostate cancer than in primary prostatic tissues [84].

Emerging new therapeutic strategies broadly target both cellular and non-cellular components of the TME more than ever, by various therapies such as immune checkpoint blockade therapy, dendritic cell vaccination, and antiangiogenic therapy [86]. Detection of RNA targets in the TME that are involved in tumor immunotherapy with the RNAscope assay can facilitate these therapies predominantly. RNAscope applications enable the determination of localization of specific immune cell types (i.e., cytotoxic lymphocytes and regulatory T cells) in the TME [87], spatial relationships between different cell types in the TME [88], and immune activation state and function of tumor-infiltrating immune cells in the TME [89,90]. For example, Monte et al., using RNAscope assay, reported that infiltrating basophils in the TME regulate tumor-promoting Th2 inflammation and reduce survival in pancreatic cancer patients [89]. Besides, this technique is an attractive strategy to determine cell type-specific expression of immune checkpoint markers [91] and differentiate activated CAR+ T cells from endogenous T cells [5]. RNAscope’s aptitude to precisely identify the cellular sources of secreted proteins (e.g., cytokines and chemokines) is a distinct benefit since although the mRNA will always localize in the cells of origin, secreted proteins tend to dilute and diffuse in the intercellular space [67,87,92]. Besides, RNAscope provides valuable information on the differentiation of paracrine and autocrine signaling, which aids in the classification of subtypes of several cancers [93]. A dual gene analysis approach with RNAscope has been utilized for simultaneous detection of CD44+ cells and PD-L1 in head and neck squamous cell carcinoma, which found that CD44+ in the TME induces expression of PD-L1, thus subsequently suppressing T cell-mediated immunity in the TME [94]. The localization and quantification of multi-RNA from several genes simultaneously by RNAscope provide greater time saving and significant results from a single feasible technique. However, rapid mRNA translation and RNA degradation in cells can affect RNAscope applications, and thus BaseScope, a subfield of RNAscope, has been recommended for short RNA targets of 50–300 nucleotides [95]. Instead of using 20 probe pairs, BaseScope utilizes short 1–6 probe pairs to target small regions of RNA more effectively. Thus BaseScope is a successful method to determine the expression and quantification of small nucleolar RNAs (snoRNAs), microRNAs, and the RNAs which have a high potential of degradation and transient expression in the TME [95].

The newest approach of RNAscope, in combination with IHC and called dual RNAscope ISH/IHC, has proven to offer an ideal platform to generate more reliable data that can be used to study gene expression signatures at the RNA and protein level with spatial and single-cell resolution in complex TME [5]. This allows correlation of both RNA and protein expression in a single slide, simultaneously validating antibody specificity [78,96,97,98]. For example, combined detection of HPV RNA by RNAscope and Cdc2 protein expression by IHC has been useful to predict the prognosis of oropharyngeal squamous cell carcinoma patients. Even more, the results conclude that the sensitivity of RNAscope was higher than that of PCR reverse dot hybridization [98]. The automated RNAscope is a significant advancement over manual RNAscope and improves the clinical advantage by allowing more samples to be analyzed in a standardized way simultaneously with less time, less inter-user variability, and less manpower in an observer-independent manner [86]. The method has proven consistent and provides reproducible results in quantifying transcript levels. Overall, the spatial resolution presented by the RNAscope method brings a novel dimension to precise localization of target RNA in single cells and allows localization and quantitation of RNA expression in specific cell types in the TME [86].

One of the most promising fields in biomarker and therapy target detection in oncology is dedicated to the exploration of the patient-specific immune contexture in situ with conventional and multiplexing IF and IHC staining techniques in combination with automated quantification [14].

One prominent approach for immune cell assessment within a particular tumor tissue, colorectal cancer (CRC), was developed by the group of Galon et al., where they successfully established a patient stratification strategy based on the detection/identification of T cell populations within the tumor core and the invasive margin named Immunoscore (ratio of the markers CD3 and CD45RO, CD3 and CD8, or CD8 and CD45RO). It is currently undergoing evaluation/implementation as a routine parameter for prognostic and predictive diagnosis in clinics for colon cancer [99,100]. To demonstrate its power the group of Pages et al. conducted a large-scale study, where his group assessed the Immunoscore by using a digital pathology method of a large patient cohort (n = 2681 CRC patients), aligned it with clinical pathological data, and thereby was able to show the power of the Immunoscore in the prognosis of survival prediction and treatment response in CRC patients [101]. In order to provide a representative (yet not complete) overview of recent applications, Table 2 shows further examples of studies using conventional and/or multiplexing IF and/or IHC staining techniques in which next-generation digital pathology was the central method for the quantification of various immune cell markers/populations in different cancer types and aligned with clinicopathological parameters.

 

The examples summarized in Table 2, as well as the example shown in Figure 2 from Desbois et al. [153] indicate the immense power of the applications of this technique utilizing next-generation digital pathology for the assessment of the immune tumor microenvironment. In order to integrate the Immunoscore or other immune cell screening strategies also into clinical research, such fully automated next-generation digital pathology platforms should be implemented into the process of quantification of the rate of infiltration of various immune cell populations/markers. Ongoing clinical studies are aiming at the integration of such platforms in combination with the staining of a set of immune-related biomarkers including main subpopulation markers and immune checkpoint markers [14].

 

To sum up, the need to automatically assess immune cell markers in situ, as well as analyzing spatial relationships, and thereby providing a better understanding of various immune cells populations and their interactions, is crucial for the detection of novel predictive and prognostic biomarkers as well as for clinical therapy strategy.

Neoangiogenesis and the resulting vascularization are equally required by the tumor, as in healthy tissues. In both types of tissue, normal and tumor cell survival and proliferation depend on oxygen and nutrition supply as well as on removal of carbon dioxide and metabolic wastes. In contrast to regulated neoangiogenesis in healthy tissues, tumor angiogenesis is characterized by an uncontrolled, ineffective, often incomplete (and therefore leaky) growth of new blood vessels within the tumor tissue in order to supply the tumor mass with oxygen and nutrition [172]. However, the in situ assessment of the density of blood vessels stained by specific markers such as CD31 or CD34 was shown to correlate with the aggressiveness of the tumor in a variety of tumor types such as CRC, breast cancer, gastric cancer, small cell and non-small cell lung cancer [173]. Furthermore, specific therapies such as neutralizing antibodies targeting anti-vascular endothelial growth factor are widely used in several cancer types [174]. However, inhibition of vessel growth has only been shown to provide limited or even no long-term improvement for cancer types including hepatocellular carcinoma and CRC [175,176]. However, the use of different non-standardized methods for detection and quantitation of blood vessel density leads to contradicting data in terms of influence on patient survival [177]. Therefore, the unbiased automated quantification of blood vessels could help to identify patient groups that would benefit from anti-angiogenic therapies.

Summarized in Table 3 are studies where next-generation digital pathology was used to detected blood vessels/blood vessel densities. Thereby we want to emphasize that the next-generation digital pathology approach is highly versatile and can be applied to various research needs and questions, not only to single cell detection or dot (RNA ISH) detection but also for the analysis of more complex structures such as blood vessels.